Radar-derived raster digital surface models (DSMs) provide a critical component for many modern applications, including flood risk analysis, telecommunications, pipeline routing, military, agriculture, and others. Interferometric Synthetic Aperture Radar (IFSAR) technologies have historically been able to produce DSMs with resolutions that range from 30 m up to 5 m depending on the sensor design and the operational parameters.
Noise gets introduced to the DSM when the DSM is processed at the same resolution as the image(s) from which the DSM is derived. The noise reduces the vertical accuracy of the data and can obscure spatial features that would otherwise be detectable. To address this issue, filtering is normally applied. However, filtering typically reduces the noise level at the expense of DSM resolution. This results in the DSM being generated at a lower resolution than the original images, e.g., as much as 4-8 times lower resolution than the image. Accordingly, there is a need for methods to recover the DSM resolution that gets lost due to filtering of the noise.